aiqbench analysis
This research introduces DLR, a framework that mitigates visual information loss in Vision-Language Models by dynamically decomposing queries and utilizing continuous visual latents for multi-step reasoning. By employing a novel Spherical Gaussian Latent Policy, the method achieves superior performance on vision-centric benchmarks while offering enhanced stepwise interpretability without relying on external tool calls.
Decompose, Look, and Reason: Reinforced Latent Reasoning for VLMs
arXiv:2604.07518v2 Announce Type: replace Abstract: Vision-Language Models often struggle with complex visual reasoning due to the visual information loss in textual CoT. Existing methods either add the cost of tool calls or rely on localized patch-based embeddings that are insufficient to extract semantics in multi-step reasoning. We propose "Decompose, Look, and Reason" (DLR), a reinforced latent reasoning framework that dynamically decomposes queries into textual premises, extracts premise-conditioned continuous visual latents, and deduces answers through grounded rationales. We introduce a three-stage training pipeline and propose a novel Spherical Gaussian Latent Policy, to enable effective exploration in the latent space. Extensive experiments on vision-centric benchmarks show that DLR consistently outperforms strong baselines, including text-only, interleaved multimodal CoT, and latent reasoning methods, while providing superior stepwise interpretability.
Abstract:Vision-Language Models often struggle with complex visual reasoning due to the visual information loss in textual CoT. Existing methods either add the cost of tool calls or rely on localized patch-based embeddings that are insufficient to extract semantics in multi-step reasoning. We propose "Decompose, Look, and Reason" (DLR), a reinforced latent reasoning framework that dynamically decomposes queries into textual premises, extracts premise-conditioned continuous visual latents, and deduces answers through grounded rationales. We introduce a three-stage training pipeline and propose a novel Spherical Gaussian Latent Policy, to enable effective exploration in the latent space. Extensive experiments on vision-centric benchmarks show that DLR consistently outperforms strong baselines, including text-only, interleaved multimodal CoT, and latent reasoning methods, while providing superior stepwise interpretability.
Computation and Language (cs.CL)
arXiv:2604.07518 [cs.CL]
(or arXiv:2604.07518v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2604.07518 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 212 words
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